benchmark_plot

library(dplyr)
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(reshape2)
library(ggpubr)
library(ComplexHeatmap)
library(circlize)
library(readxl)
library(plyr)
library(knitr)
library(stargazer)
setwd("~/Dropbox (Sydney Uni)/YunweiZhang/WeeklyProgress/R code/benchmarking/202012summaryresult")

• this moved from 202011fromserverold2, this is the new summary report to generate plots (* this is the new summarised plot for picked methods (based on benchmark_plot5,6,7))

1 data

plot_fun=function(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1, coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm){
cox1dt=bind_rows(cox1, .id = "column_label")
cox1value=colMeans(cox1dt[,-1],na.rm = TRUE)
cox2dt=bind_rows(cox2, .id = "column_label")
cox2value=colMeans(cox2dt[,-1],na.rm = TRUE)
cox3dt=bind_rows(cox3, .id = "column_label")
cox3value=colMeans(cox3dt[,-1],na.rm = TRUE)
#this contains NA 
cox4=cox4[lapply(cox4,length)>1]
cox4dt=bind_rows(cox4, .id = "column_label")
cox4value=colMeans(cox4dt[,-1],na.rm = TRUE)
#cox5dt=bind_rows(cox5, .id = "column_label")
#cox5value=colMeans(cox5dt[,-1],na.rm = TRUE)
#cox6dt=bind_rows(cox6, .id = "column_label")
#cox6value=colMeans(cox6dt[,-1],na.rm = TRUE)
pcox1dt=bind_rows(pcox1, .id = "column_label")
pcox1value=colMeans(pcox1dt[,-1],na.rm = TRUE)
pcox2dt=bind_rows(pcox2, .id = "column_label")
pcox2value=colMeans(pcox2dt[,-1],na.rm = TRUE)
pcox3dt=bind_rows(pcox3, .id = "column_label")
pcox3value=colMeans(pcox3dt[,-1],na.rm = TRUE)
rsf1dt=bind_rows(rsf1, .id = "column_label")
rsf1value=colMeans(rsf1dt[,-1],na.rm = TRUE)
rsf2dt=bind_rows(rsf2, .id = "column_label")
rsf2value=colMeans(rsf2dt[,-1],na.rm = TRUE)
mtlr1dt=bind_rows(mtlr1, .id = "column_label")
mtlr1value=colMeans(mtlr1dt[,-1],na.rm = TRUE)
dnnsurv1dt=bind_rows(dnnsurv1, .id = "column_label")
dnnsurv1value=colMeans(dnnsurv1dt[,-1],na.rm = TRUE)
coxboostdt=bind_rows(coxboost, .id = "column_label")
coxboostvalue=colMeans(coxboostdt[,-1],na.rm = TRUE)
gacoxdt=bind_rows(gacox, .id = "column_label")
gacoxvalue=colMeans(gacoxdt[,-1],na.rm = TRUE)
gamtlrdt=bind_rows(gamtlr, .id = "column_label")
gamtlrvalue=colMeans(gamtlrdt[,-1],na.rm = TRUE)
gacoxboostdt=bind_rows(gacoxboost, .id = "column_label")
gacoxboostvalue=colMeans(gacoxboostdt[,-1],na.rm = TRUE)
limmamtlrdt=bind_rows(limmamtlr, .id = "column_label")
limmamtlrvalue=colMeans(limmamtlrdt[,-1],na.rm = TRUE)
limmacoxboostdt=bind_rows(limmacoxboost, .id = "column_label")
limmacoxboostvalue=colMeans(limmacoxboostdt[,-1],na.rm = TRUE)
survivalsvmdt=bind_rows(survivalsvm, .id = "column_label")
survivalsvmvalue=colMeans(survivalsvmdt[,-1],na.rm = TRUE)





nb.cols <- 18
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)

plotdt1=rbind.data.frame(cox1value,cox2value,cox3value,cox4value,pcox1value,pcox2value,pcox3value,rsf1value,rsf2value,mtlr1value,dnnsurv1value,coxboostvalue,gacoxvalue,gamtlrvalue,gacoxboostvalue,limmamtlrvalue,limmacoxboostvalue,survivalsvmvalue)
rownames(plotdt1)=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
colnames(plotdt1)=names(pcox1value)
head(plotdt1)
plotdt2=t(plotdt1)
#head(plotdt2)
#plotdt3=apply(plotdt2, MARGIN = 2, FUN = function(X) (X - min(X))/diff(range(X)))
plotdt3=plotdt2[-c(2,4,5:10),]
#plotdt3[plotdt3>1]=NA #out of range (0,1) is definde as NA
breaksList = seq(0.5, 1, by = 0.1)
#pheatmap(as.matrix(plotdt3),cellwidth = 15, cellheight = 15, fontsize = 8,cluster_rows = FALSE,cluster_cols = FALSE,color =colorRampPalette(c("#999999", "#E69F00", "#56B4E9"))(length(breaksList)),breaks = breaksList)
p1=pheatmap(as.matrix(plotdt3),cellwidth = 15, cellheight = 15, fontsize = 8,cluster_rows = FALSE,cluster_cols = FALSE,color =colorRampPalette(rev(brewer.pal(n = 7, name = "Pastel1")))(length(breaksList)),breaks = breaksList)
#print(p1)

# create data frame with all values
colnames(cox1dt)=colnames(cox2dt)=colnames(cox3dt)=colnames(cox4dt)=colnames(pcox1dt)=colnames(pcox2dt)=colnames(pcox3dt)=colnames(rsf1dt)=colnames(rsf2dt)=colnames(mtlr1dt)=colnames(dnnsurv1dt)=colnames(coxboostdt)=colnames(gacoxdt)=colnames(gamtlrdt)=colnames(gacoxboostdt)=colnames(limmamtlrdt)=colnames(limmacoxboostdt)=colnames(survivalsvmdt)=c("model","hc","bc","unoc","ghc","bs1","bs2","bs3","bs4","bs5","bs6","auc1","auc2","auc3","auc4","auc5","auc6","auc7","auc8","auc9","auc10","auc11","auc12","auc13","auc14","auc15","auc")
widedt=do.call("rbind", list(cox1dt,cox2dt,cox3dt,cox4dt,pcox1dt,pcox2dt,pcox3dt,rsf1dt,rsf2dt,mtlr1dt,dnnsurv1dt,coxboostdt,gacoxdt,gamtlrdt,gacoxboostdt,limmamtlrdt,limmacoxboostdt,survivalsvmdt))
widedt[,1]=c(rep("cox1",nrow(cox1dt)),rep("cox2",nrow(cox2dt)),rep("cox3",nrow(cox3dt)),rep("cox4",nrow(cox4dt)),rep("pcox1",nrow(pcox1dt)),rep("pcox2",nrow(pcox2dt)),rep("pcox3",nrow(pcox3dt)),rep("rsf1",nrow(rsf1dt)),rep("rsf2",nrow(rsf2dt)),rep("mtlr1",nrow(mtlr1dt)),rep("dnnsurv1",nrow(dnnsurv1dt)),rep("coxboost",nrow(coxboostdt)),rep("gacox",nrow(gacoxdt)),rep("gamtlr",nrow(gamtlrdt)),rep("gacoxboost",nrow(gacoxboostdt)),rep("limmamtlr",nrow(limmamtlrdt)),rep("limmacoxboost",nrow(limmacoxboostdt)),rep("survivalsvm",nrow(survivalsvmdt)))
# grouped boxplot
longdt=melt(widedt,id.vars = "model",na.rm = TRUE)
#dim(longdt)
#any(is.na(longdt))

# get the hc
plotdt=longdt[longdt$variable=="hc",]
#plotdt[plotdt$value <0.5 | plotdt$value >0.99,3] <- NA
#plotdt[plotdt$value < quantile(plotdt$value, 0.85,na.rm = T) | plotdt$value > quantile(plotdt$value, 0.15,na.rm = T), ]

plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm"))
g1=ggplot(plotdt, aes(x=model, y=value, fill=model)) + geom_boxplot(lwd=0.5)+scale_fill_manual(values = mycolors,drop=FALSE) #+scale_y_continuous(limits=c(0.5,1))

# get all the cindex
plotdt=longdt[longdt$variable=="hc"|longdt$variable=="bc"|longdt$variable=="unoc"|longdt$variable=="ghc",]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm"))
g2=ggplot(plotdt, aes(x=model, y=value, fill=variable)) + geom_boxplot(lwd=0.5)+scale_fill_manual(values = mycolors) #+scale_y_continuous(limits=c(0.5,1))

# get all the auc
plotdt=longdt[longdt$variable=="auc1"|longdt$variable=="auc2"|longdt$variable=="auc3"|longdt$variable=="auc4",]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm"))
g3=ggplot(plotdt, aes(x=model, y=value, fill=variable)) + geom_boxplot(lwd=0.5)+scale_fill_manual(values = mycolors, drop=FALSE) #+scale_y_continuous(limits=c(0.5,1))

# get all the bs
plotdt=longdt[longdt$variable=="bs1"|longdt$variable=="bs2",]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm"))
g4=ggplot(plotdt, aes(x=model, y=value, fill=variable)) + geom_boxplot(lwd=0.1,outlier.shape = NA)+scale_fill_manual(values = mycolors) 

# get bs1
plotdt=longdt[longdt$variable=="bs1",]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm"))
g5=ggplot(plotdt, aes(x=model, y=value, fill=model)) + geom_boxplot(lwd=0.5,outlier.shape = NA)+scale_fill_manual(values = mycolors, drop=FALSE) 



# get auc curve
plotdt=longdt[longdt$variable=="auc1"|longdt$variable=="auc2"|longdt$variable=="auc3"|longdt$variable=="auc4"|longdt$variable=="auc5"|longdt$variable=="auc6"|longdt$variable=="auc7"|longdt$variable=="auc8"|longdt$variable=="auc9"|longdt$variable=="auc10"|longdt$variable=="auc11"|longdt$variable=="auc12"|longdt$variable=="auc13"|longdt$variable=="auc14"|longdt$variable=="auc15",]
#plotdt[plotdt$value >1 | plotdt$value==0,3] <- NA

plotdt2 <- plotdt %>%dplyr::group_by(variable,model) %>%dplyr::summarize(Mean = mean(value))
plotdt2$timepoint=as.numeric(gsub(".*?([0-9]+).*", "\\1", plotdt2$variable))
# g6=ggplot(data = plotdt2, aes(timepoint, Mean)) +
#   geom_line() +geom_smooth()+
#   labs(title = "                                                            time-dependent AUC",
#        y = "value", x = "time points") + 
#   facet_wrap(~ model)#+scale_y_continuous(limits=c(0,1))

g6=ggplot(plotdt2,aes(x=timepoint,y=Mean,color=model))+geom_line(aes(color=model))+scale_color_manual(values = mycolors, drop=FALSE) 
# print(g1)
# print(g2)
# print(g3)
# print(g4)
# print(g5)
# print(g6)
#print(ggarrange(g1,g2, g3,g4,g5,labels = c("c","c", "auc", "br","ibr"),ncol = 2, nrow = 3))
return(list(longdt,g1,g2,g3,g4,g5,g6,p1))
}

characteristic_table=function(cox4){
cox4dt=bind_rows(cox4, .id = "column_label")
colnames(cox4dt)=c("model","hc","bc","unoc","ghc","bs1","bs2","bs3","bs4","bs5","bs6","auc1","auc2","auc3","auc4","auc5","auc6","auc7","auc8","auc9","auc10","auc11","auc12","auc13","auc14","auc15","auc")
proportion=NULL
summaryy=NULL
if(sum(rowSums(is.na(cox4dt))==26)!=0){
  proportion=1-sum(rowSums(is.na(cox4dt))==26)/100
cox4dt=cox4dt[-which(rowSums(is.na(cox4dt))==26),]
summaryy=summary(cox4dt)}else{
  proportion=1
  summaryy=summary(cox4dt)
}
return(list(proportion,summaryy))
}

1.1 pbc

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/pbc_cox1.rds")
cox2=readRDS("savedresults/pbc_bw_cox1.rds")
cox3=readRDS("savedresults/pbc_bw_cox2.rds")
cox4=readRDS("savedresults/pbc_bw_cox3.rds")
pcox1=readRDS("savedresults/pbc_p_cox1.rds")
pcox2=readRDS("savedresults/pbc_p_cox2.rds")
pcox3=readRDS("savedresults/pbc_p_cox3.rds")
rsf1=readRDS("savedresults/pbc_rsf1.rds")
rsf2=readRDS("savedresults/pbc_rsf2.rds")
mtlr1=readRDS("savedresults/pbc_mtlr.rds")
dnnsurv1=readRDS("savedresults/pbc_dnnsurv.rds")
coxboost=readRDS("savedresults/pbc_coxboost.rds")
gacox=empty_list
gamtlr=empty_list
gacoxboost=empty_list
limmamtlr=empty_list
limmacoxboost=empty_list
survivalsvm=readRDS("savedresults/pbc_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

pbcfull=want[[1]]
g1=want[[2]]
g2=want[[3]]
g3=want[[4]]
g4=want[[5]]
g5=want[[6]]
g6=want[[7]]
g7=want[[8]]
characteristic_table(dnnsurv1)[[1]]

## [1] 0.8

1.2 veteran

cox1=readRDS("savedresults/veteran_cox1.rds")
cox2=readRDS("savedresults/veteran_bw_cox1.rds")
cox3=readRDS("savedresults/veteran_bw_cox2.rds")
cox4=readRDS("savedresults/veteran_bw_cox3.rds")
pcox1=readRDS("savedresults/veteran_p_cox1.rds")
pcox2=readRDS("savedresults/veteran_p_cox2.rds")
pcox3=readRDS("savedresults/veteran_p_cox3.rds")
rsf1=readRDS("savedresults/veteran_rsf1.rds")
rsf2=readRDS("savedresults/veteran_rsf2.rds")
mtlr1=readRDS("savedresults/veteran_mtlr.rds")
dnnsurv1=readRDS("savedresults/veteran_dnnsurv.rds")
coxboost=readRDS("savedresults/veteran_coxboost.rds")
gacox=empty_list
gamtlr=empty_list
gacoxboost=empty_list
limmamtlr=empty_list
limmacoxboost=empty_list
survivalsvm=readRDS("savedresults/veteran_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

veteranfull=want[[1]]
g8=want[[2]]
g9=want[[3]]
g10=want[[4]]
g11=want[[5]]
g12=want[[6]]
g13=want[[7]]
g14=want[[8]]

characteristic_table(dnnsurv1)[[1]]

## [1] 0.66

1.3 lung

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/lung_cox1.rds")
cox2=readRDS("savedresults/lung_bw_cox1.rds")
cox3=readRDS("savedresults/lung_bw_cox2.rds")
cox4=readRDS("savedresults/lung_bw_cox3.rds")
pcox1=readRDS("savedresults/lung_p_cox1.rds")
pcox2=readRDS("savedresults/lung_p_cox2.rds")
pcox3=readRDS("savedresults/lung_p_cox3.rds")
rsf1=readRDS("savedresults/lung_rsf1.rds")
rsf2=readRDS("savedresults/lung_rsf2.rds")
mtlr1=readRDS("savedresults/lung_mtlr.rds")
dnnsurv1=readRDS("savedresults/lung_dnnsurv.rds")
coxboost=readRDS("savedresults/lung_coxboost.rds")
gacox=empty_list
gamtlr=empty_list
gacoxboost=empty_list
limmamtlr=empty_list
limmacoxboost=empty_list
survivalsvm=readRDS("savedresults/lung_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

lungfull=want[[1]]
g15=want[[2]]
g16=want[[3]]
g17=want[[4]]
g18=want[[5]]
g19=want[[6]]
g20=want[[7]]
g21=want[[8]]
characteristic_table(dnnsurv1)[[1]]

## [1] 1

1.4 ANZ

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/anz_cox1.rds")
cox2=readRDS("savedresults/anz_bw_cox1.rds")
cox3=readRDS("savedresults/anz_bw_cox2.rds")
cox4=readRDS("savedresults/anz_bw_cox3.rds")
pcox1=readRDS("savedresults/anz_p_cox1.rds")
pcox2=readRDS("savedresults/anz_p_cox2.rds")
pcox3=readRDS("savedresults/anz_p_cox3.rds")
rsf1=readRDS("savedresults/anz_rsf1.rds")
rsf2=readRDS("savedresults/anz_rsf2.rds")
mtlr1=readRDS("savedresults/anz_mtlr.rds")
dnnsurv1=readRDS("savedresults/anz_dnnsurv.rds")
coxboost=readRDS("savedresults/anz_coxboost.rds")
gacox=empty_list
gamtlr=empty_list
gacoxboost=empty_list
limmamtlr=empty_list
limmacoxboost=empty_list
survivalsvm=empty_list
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

anzfull=want[[1]]
g22=want[[2]]
g23=want[[3]]
g24=want[[4]]
g25=want[[5]]
g26=want[[6]]
g27=want[[7]]
g28=want[[8]]



characteristic_table(dnnsurv1)[[1]]

## [1] 1

#characteristic_table(pcox3)

1.5 US

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/us_cox1.rds")
cox2=empty_list
cox3=empty_list
cox4=empty_list
#pcox1=readRDS("US/cox4.rds")
 # for (i in 1:length(dnnsurv1)){
 #    if (class(dnnsurv1[[i]])=="try-error")
 #    dnnsurv1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
 #    colnames(dnnsurv1[[i]])=c("hc", "bc", "unoc" ,"ghc" , "br1" , "br2" ,"br3","br4",  "br5", "br6" , "a1"  , "a2","a3" , "a4","a5" , "a6", "a7", "a8", "a9", "a10" , "a11" , "a12" , "a13",  "a14" , "a15" , "a")}
pcox1=readRDS("savedresults/us_p_cox1.rds")
pcox2=readRDS("savedresults/us_p_cox2.rds")
pcox3=readRDS("savedresults/us_p_cox3.rds")
rsf1=readRDS("savedresults/us_rsf1.rds")
rsf2=readRDS("savedresults/us_rsf2.rds")
mtlr1=readRDS("savedresults/us_mtlr.rds")
dnnsurv1=empty_list
coxboost=readRDS("savedresults/us_coxboost.rds")
gacox=empty_list
gamtlr=empty_list
gacoxboost=empty_list
limmamtlr=empty_list
limmacoxboost=empty_list
survivalsvm=empty_list
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

usfull=want[[1]]
g29=want[[2]]
g30=want[[3]]
g31=want[[4]]
g32=want[[5]]
g33=want[[6]]
g34=want[[7]]
g35=want[[8]]

#characteristic_table(rsf1)[[1]]
characteristic_table(pcox3)[[1]]

## [1] 1

1.6 melanoma_clinical

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/melanomaclinical_cox1.rds")
cox2=empty_list
cox3=empty_list
cox4=empty_list
#pcox1=readRDS("US/cox4.rds")
pcox1=readRDS("savedresults/melanomaclinical_p_cox1.rds")
pcox2=readRDS("savedresults/melanomaclinical_p_cox2.rds")
pcox3=readRDS("savedresults/melanomaclinical_p_cox3.rds")
rsf1=readRDS("savedresults/melanomaclinical_rsf1.rds")
rsf2=readRDS("savedresults/melanomaclinical_rsf2.rds")
mtlr1=readRDS("savedresults/melanomaclinical_mtlr.rds")
#dnnsurv1=readRDS("savedresults/melanomaclinical_dnnsurv.rds")
dnnsurv1=empty_list
# summ=0
#  for (i in 1:length(dnnsurv1)){
#     if (class(dnnsurv1[[i]])=="try-error"){
#       summ=summ+1
#     dnnsurv1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
#     colnames(dnnsurv1[[i]])=c("hc", "bc", "unoc" ,"ghc" , "br1" , "br2" ,"br3","br4",  "br5", "br6" , "a1"  , "a2","a3" , "a4","a5" , "a6", "a7", "a8", "a9", "a10" , "a11" , "a12" , "a13",  "a14" , "a15" , "a")}
# print(summ)

coxboost=readRDS("savedresults/melanomaclinica_coxboost.rds")
gacox=empty_list
gamtlr=empty_list
gacoxboost=empty_list
limmamtlr=empty_list
limmacoxboost=empty_list
survivalsvm=readRDS("savedresults/clinical_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

melanoma_clinicalfull=want[[1]]
g36=want[[2]]
g37=want[[3]]
g38=want[[4]]
g39=want[[5]]
g40=want[[6]]
g41=want[[7]]
g42=want[[8]]

#characteristic_table(dnnsurv1)[[1]]

1.7 melanoma_itraq

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
# cox2=readRDS("melanomaitraq2/melanomaitraq_bw_cox1.rds")
# summ=0
#  for (i in 1:length(cox2)){
#     if (class(cox2[[i]])=="try-error"){
#       summ=summ+1
#     cox2[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
#     colnames(cox2[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
# print(summ)
# cox3=readRDS("melanomaitraq2/melanomaitraq_bw_cox2.rds")
# summ=0
#  for (i in 1:length(cox3)){
#     if (class(cox3[[i]])=="try-error"){
#       summ=summ+1
#     cox3[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
#     colnames(cox3[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
# print(summ)
# cox4=readRDS("melanomaitraq2/melanomaitraq_bw_cox3.rds")
# summ=0
#  for (i in 1:length(cox4)){
#     if (class(cox4[[i]])=="try-error"){
#       summ=summ+1
#     cox4[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
#     colnames(cox4[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
# print(summ)
#pcox1=readRDS("US/cox4.rds")
pcox1=readRDS("savedresults/melanomaitraq_p_cox1.rds")
pcox2=empty_list
pcox3=empty_list
rsf1=readRDS("savedresults/melanomaitraq_rsf1.rds")
rsf2=readRDS("savedresults/melanomaitraq_rsf2.rds")
#mtlr1=readRDS("melanomaitraq2/melanomaitraq_mtlr.rds")
#can no longer run
mtlr1=empty_list
dnnsurv1=readRDS("savedresults/melanomaitraq_dnnsurv.rds")

# rsf1=rsf1$value
# rsf2=rsf2$value
# mtlr1=mtlr1$value
coxboost=readRDS("savedresults/melanomaitraq_coxboost.rds")
gacox=readRDS("savedresults/itraq_ga_cox1.rds")
gamtlr=readRDS("savedresults/melanomaitraq_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/melanomaitraq_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/melanomaitraq_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/melanomaitraq_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/itraq_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

melanoma_itraqfull=want[[1]]
g43=want[[2]]
g44=want[[3]]
g45=want[[4]]
g46=want[[5]]
g47=want[[6]]
g48=want[[7]]
g49=want[[8]]

#characteristic_table(rsf1)[[2]]
characteristic_table(dnnsurv1)[[1]]

## [1] 0.63

1.8 melanoma_swath

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
#pcox1=readRDS("US/cox4.rds")
pcox1=readRDS("savedresults/melanomaswath_p_cox1.rds")
pcox2=empty_list
pcox3=empty_list
rsf1=readRDS("savedresults/melanomaswath_rsf1.rds")
rsf2=readRDS("savedresults/melanomaswath_rsf2.rds")
mtlr1=readRDS("savedresults/melanomaswath_mtlr.rds")
dnnsurv1=empty_list
coxboost=readRDS("savedresults/melanomaswath_coxboost.rds")

gacox=readRDS("savedresults/melanomaswath_ga_cox1.rds")
gamtlr=readRDS("savedresults/melanomaswath_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/melanomaswath_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/melanomaswath_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/melanomaswath_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/melanomaswath_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

melanoma_swathfull=want[[1]]
g50=want[[2]]
g51=want[[3]]
g52=want[[4]]
g53=want[[5]]
g54=want[[6]]
g55=want[[7]]
g56=want[[8]]

1.9 melanomanano

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=readRDS("savedresults/melanomanano_bw_cox1.rds")
summ=0
 for (i in 1:length(cox2)){
    if (class(cox2[[i]])=="try-error"){
      summ=summ+1
    cox2[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
    colnames(cox2[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 15

cox3=readRDS("savedresults/melanomanano_bw_cox2.rds")
summ=0
 for (i in 1:length(cox3)){
    if (class(cox3[[i]])=="try-error"){
      summ=summ+1
    cox3[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
    colnames(cox3[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 18

cox4=readRDS("savedresults/melanomanano_bw_cox3.rds")
summ=0
 for (i in 1:length(cox4)){
    if (class(cox4[[i]])=="try-error"){
      summ=summ+1
    cox4[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
    colnames(cox4[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 11

pcox1=readRDS("savedresults/melanomanano_p_cox1.rds")
summ=0
 for (i in 1:length(pcox1)){
    if (class(pcox1[[i]])=="try-error"){
      summ=summ+1
    pcox1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{pcox1[[i]]=pcox1[[i]]}
    colnames(pcox1[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 0

pcox2=readRDS("savedresults/melanomanano_p_cox2.rds")
summ=0
 for (i in 1:length(pcox2)){
    if (class(pcox2[[i]])=="try-error"){
      summ=summ+1
    pcox2[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{pcox2[[i]]=pcox2[[i]]}
    colnames(pcox2[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 0

pcox3=readRDS("savedresults/melanomanano_p_cox3.rds")
summ=0
 for (i in 1:length(pcox3)){
    if (class(pcox3[[i]])=="try-error"){
      summ=summ+1
    pcox3[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{pcox3[[i]]=pcox3[[i]]}
    colnames(pcox3[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 0

rsf1=readRDS("savedresults/melanomanano_rsf1.rds")
summ=0
 for (i in 1:length(rsf1)){
    if (class(rsf1[[i]])=="try-error"){
      summ=summ+1
    rsf1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{rsf1[[i]]=rsf1[[i]]}
    colnames(rsf1[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 0

rsf2=readRDS("savedresults/melanomanano_rsf2.rds")
summ=0
 for (i in 1:length(rsf2)){
    if (class(rsf2[[i]])=="try-error"){
      summ=summ+1
    rsf2[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{rsf2[[i]]=rsf2[[i]]}
    colnames(rsf2[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 0

mtlr1=readRDS("savedresults/melanomanano_mtlr.rds")
summ=0
 for (i in 1:length(mtlr1)){
    if (class(mtlr1[[i]])=="try-error"){
      summ=summ+1
    mtlr1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{mtlr1[[i]]=mtlr1[[i]]}
    colnames(mtlr1[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 0

dnnsurv1=readRDS("savedresults/melanomanano_dnnsurv.rds")
summ=0
 for (i in 1:length(dnnsurv1)){
    if (class(dnnsurv1[[i]])=="try-error"){
      summ=summ+1
    dnnsurv1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{dnnsurv1[[i]]=dnnsurv1[[i]]}
    colnames(dnnsurv1[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 0

coxboost=readRDS("savedresults/melanomanano_coxboost.rds")
summ=0
 for (i in 1:length(coxboost)){
    if (class(coxboost[[i]])=="try-error"){
      summ=summ+1
    coxboost[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{coxboost[[i]]=coxboost[[i]]}
    colnames(coxboost[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")}
print(summ)

## [1] 0

gacox=empty_list
gamtlr=empty_list
gacoxboost=empty_list
limmamtlr=empty_list
limmacoxboost=empty_list
survivalsvm=empty_list
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

melanomananofull=want[[1]]
g57=want[[2]]
g58=want[[3]]
g59=want[[4]]
g60=want[[5]]
g61=want[[6]]
g62=want[[7]]
g63=want[[8]]

characteristic_table(rsf1)[[2]]

##     model                 hc            bc           unoc        ghc     
##  Length:100         Min.   :0.5   Min.   : NA   Min.   :0   Min.   : NA  
##  Class :character   1st Qu.:0.5   1st Qu.: NA   1st Qu.:0   1st Qu.: NA  
##  Mode  :character   Median :0.5   Median : NA   Median :0   Median : NA  
##                     Mean   :0.5   Mean   :NaN   Mean   :0   Mean   :NaN  
##                     3rd Qu.:0.5   3rd Qu.: NA   3rd Qu.:0   3rd Qu.: NA  
##                     Max.   :0.5   Max.   : NA   Max.   :0   Max.   : NA  
##                                   NA's   :100               NA's   :100  
##       bs1             bs2             bs3              bs4        
##  Min.   :1.355   Min.   :1.355   Min.   :0.3441   Min.   :0.2066  
##  1st Qu.:1.806   1st Qu.:1.806   1st Qu.:0.3441   1st Qu.:0.2340  
##  Median :1.934   Median :1.934   Median :0.3441   Median :0.2475  
##  Mean   :2.065   Mean   :2.065   Mean   :0.3441   Mean   :0.2556  
##  3rd Qu.:2.184   3rd Qu.:2.184   3rd Qu.:0.3441   3rd Qu.:0.2647  
##  Max.   :5.089   Max.   :5.089   Max.   :0.3441   Max.   :0.5010  
##                                  NA's   :99                       
##       bs5              bs6              auc1          auc2          auc3    
##  Min.   :0.2066   Min.   :0.2066   Min.   :0.5   Min.   :0.5   Min.   :0.5  
##  1st Qu.:0.2340   1st Qu.:0.2340   1st Qu.:0.5   1st Qu.:0.5   1st Qu.:0.5  
##  Median :0.2475   Median :0.2475   Median :0.5   Median :0.5   Median :0.5  
##  Mean   :0.2556   Mean   :0.2556   Mean   :0.5   Mean   :0.5   Mean   :0.5  
##  3rd Qu.:0.2647   3rd Qu.:0.2647   3rd Qu.:0.5   3rd Qu.:0.5   3rd Qu.:0.5  
##  Max.   :0.5010   Max.   :0.5010   Max.   :0.5   Max.   :0.5   Max.   :0.5  
##                                                                             
##       auc4            auc5            auc6            auc7      
##  Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
##  1st Qu.:0.500   1st Qu.:0.500   1st Qu.:0.500   1st Qu.:0.500  
##  Median :0.500   Median :0.500   Median :0.500   Median :0.500  
##  Mean   :0.495   Mean   :0.495   Mean   :0.495   Mean   :0.495  
##  3rd Qu.:0.500   3rd Qu.:0.500   3rd Qu.:0.500   3rd Qu.:0.500  
##  Max.   :0.500   Max.   :0.500   Max.   :0.500   Max.   :0.500  
##                                                                 
##       auc8            auc9           auc10           auc11          auc12      
##  Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.00   Min.   :0.000  
##  1st Qu.:0.500   1st Qu.:0.500   1st Qu.:0.500   1st Qu.:0.50   1st Qu.:0.500  
##  Median :0.500   Median :0.500   Median :0.500   Median :0.50   Median :0.500  
##  Mean   :0.495   Mean   :0.495   Mean   :0.495   Mean   :0.49   Mean   :0.485  
##  3rd Qu.:0.500   3rd Qu.:0.500   3rd Qu.:0.500   3rd Qu.:0.50   3rd Qu.:0.500  
##  Max.   :0.500   Max.   :0.500   Max.   :0.500   Max.   :0.50   Max.   :0.500  
##                                                                                
##      auc13           auc14           auc15           auc        
##  Min.   :0.000   Min.   :0.000   Min.   :0.00   Min.   :0.3889  
##  1st Qu.:0.500   1st Qu.:0.500   1st Qu.:0.50   1st Qu.:0.5000  
##  Median :0.500   Median :0.500   Median :0.50   Median :0.5000  
##  Mean   :0.485   Mean   :0.485   Mean   :0.46   Mean   :0.4983  
##  3rd Qu.:0.500   3rd Qu.:0.500   3rd Qu.:0.50   3rd Qu.:0.5000  
##  Max.   :0.500   Max.   :0.500   Max.   :0.50   Max.   :0.5000  
## 

characteristic_table(pcox1)[[1]]

## [1] 1

characteristic_table(pcox1)[[2]]

##     model                 hc               bc              unoc       
##  Length:100         Min.   :0.1818   Min.   :0.2129   Min.   :0.1818  
##  Class :character   1st Qu.:0.4615   1st Qu.:0.4721   1st Qu.:0.4654  
##  Mode  :character   Median :0.5667   Median :0.5538   Median :0.5667  
##                     Mean   :0.5699   Mean   :0.5529   Mean   :0.5742  
##                     3rd Qu.:0.6667   3rd Qu.:0.6250   3rd Qu.:0.6667  
##                     Max.   :1.0000   Max.   :0.7991   Max.   :1.0000  
##                                                                       
##       ghc              bs1             bs2             bs3           bs4     
##  Min.   :0.5089   Min.   :1.096   Min.   :1.096   Min.   : NA   Min.   : NA  
##  1st Qu.:0.5616   1st Qu.:1.677   1st Qu.:1.677   1st Qu.: NA   1st Qu.: NA  
##  Median :0.6241   Median :1.940   Median :1.940   Median : NA   Median : NA  
##  Mean   :0.6362   Mean   :2.042   Mean   :2.042   Mean   :NaN   Mean   :NaN  
##  3rd Qu.:0.6945   3rd Qu.:2.254   3rd Qu.:2.254   3rd Qu.: NA   3rd Qu.: NA  
##  Max.   :0.8353   Max.   :4.387   Max.   :4.387   Max.   : NA   Max.   : NA  
##                                                   NA's   :100   NA's   :100  
##       bs5           bs6           auc1             auc2             auc3       
##  Min.   : NA   Min.   : NA   Min.   :0.0000   Min.   :0.0000   Min.   :0.0500  
##  1st Qu.: NA   1st Qu.: NA   1st Qu.:0.3333   1st Qu.:0.4333   1st Qu.:0.5000  
##  Median : NA   Median : NA   Median :0.5000   Median :0.5556   Median :0.6111  
##  Mean   :NaN   Mean   :NaN   Mean   :0.5285   Mean   :0.5573   Mean   :0.5993  
##  3rd Qu.: NA   3rd Qu.: NA   3rd Qu.:0.7222   3rd Qu.:0.7036   3rd Qu.:0.7143  
##  Max.   : NA   Max.   : NA   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##  NA's   :100   NA's   :100                                                     
##       auc4             auc5             auc6             auc7       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.5000   1st Qu.:0.5000   1st Qu.:0.5000   1st Qu.:0.5000  
##  Median :0.6111   Median :0.6111   Median :0.6111   Median :0.6111  
##  Mean   :0.6042   Mean   :0.6042   Mean   :0.6042   Mean   :0.6042  
##  3rd Qu.:0.7500   3rd Qu.:0.7500   3rd Qu.:0.7500   3rd Qu.:0.7500  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##                                                                     
##       auc8             auc9            auc10            auc11       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.4917   1st Qu.:0.5000   1st Qu.:0.5000   1st Qu.:0.5000  
##  Median :0.6111   Median :0.6111   Median :0.6548   Median :0.6667  
##  Mean   :0.6029   Mean   :0.6078   Mean   :0.6245   Mean   :0.6237  
##  3rd Qu.:0.7500   3rd Qu.:0.7500   3rd Qu.:0.7569   3rd Qu.:0.7625  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##                                                                     
##      auc12            auc13            auc14            auc15       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.5066   1st Qu.:0.5066   1st Qu.:0.5066   1st Qu.:0.4000  
##  Median :0.7071   Median :0.7071   Median :0.7071   Median :0.6181  
##  Mean   :0.6636   Mean   :0.6636   Mean   :0.6636   Mean   :0.5889  
##  3rd Qu.:0.8333   3rd Qu.:0.8333   3rd Qu.:0.8333   3rd Qu.:0.8185  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##                                                                     
##       auc         
##  Min.   :0.07751  
##  1st Qu.:0.45509  
##  Median :0.54991  
##  Mean   :0.57606  
##  3rd Qu.:0.70616  
##  Max.   :1.00000  
## 

characteristic_table(pcox3)[[2]]

##     model                 hc               bc              unoc       
##  Length:100         Min.   :0.1818   Min.   :0.2129   Min.   :0.1818  
##  Class :character   1st Qu.:0.4615   1st Qu.:0.4721   1st Qu.:0.4654  
##  Mode  :character   Median :0.5667   Median :0.5538   Median :0.5667  
##                     Mean   :0.5699   Mean   :0.5529   Mean   :0.5742  
##                     3rd Qu.:0.6667   3rd Qu.:0.6250   3rd Qu.:0.6667  
##                     Max.   :1.0000   Max.   :0.7991   Max.   :1.0000  
##                                                                       
##       ghc              bs1             bs2             bs3           bs4     
##  Min.   :0.5089   Min.   :1.096   Min.   :1.096   Min.   : NA   Min.   : NA  
##  1st Qu.:0.5616   1st Qu.:1.677   1st Qu.:1.677   1st Qu.: NA   1st Qu.: NA  
##  Median :0.6241   Median :1.940   Median :1.940   Median : NA   Median : NA  
##  Mean   :0.6362   Mean   :2.042   Mean   :2.042   Mean   :NaN   Mean   :NaN  
##  3rd Qu.:0.6945   3rd Qu.:2.254   3rd Qu.:2.254   3rd Qu.: NA   3rd Qu.: NA  
##  Max.   :0.8353   Max.   :4.387   Max.   :4.387   Max.   : NA   Max.   : NA  
##                                                   NA's   :100   NA's   :100  
##       bs5           bs6           auc1             auc2             auc3       
##  Min.   : NA   Min.   : NA   Min.   :0.0000   Min.   :0.0000   Min.   :0.0500  
##  1st Qu.: NA   1st Qu.: NA   1st Qu.:0.3333   1st Qu.:0.4333   1st Qu.:0.5000  
##  Median : NA   Median : NA   Median :0.5000   Median :0.5556   Median :0.6111  
##  Mean   :NaN   Mean   :NaN   Mean   :0.5285   Mean   :0.5573   Mean   :0.5993  
##  3rd Qu.: NA   3rd Qu.: NA   3rd Qu.:0.7222   3rd Qu.:0.7036   3rd Qu.:0.7143  
##  Max.   : NA   Max.   : NA   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##  NA's   :100   NA's   :100                                                     
##       auc4             auc5             auc6             auc7       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.5000   1st Qu.:0.5000   1st Qu.:0.5000   1st Qu.:0.5000  
##  Median :0.6111   Median :0.6111   Median :0.6111   Median :0.6111  
##  Mean   :0.6042   Mean   :0.6042   Mean   :0.6042   Mean   :0.6042  
##  3rd Qu.:0.7500   3rd Qu.:0.7500   3rd Qu.:0.7500   3rd Qu.:0.7500  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##                                                                     
##       auc8             auc9            auc10            auc11       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.4917   1st Qu.:0.5000   1st Qu.:0.5000   1st Qu.:0.5000  
##  Median :0.6111   Median :0.6111   Median :0.6548   Median :0.6667  
##  Mean   :0.6029   Mean   :0.6078   Mean   :0.6245   Mean   :0.6237  
##  3rd Qu.:0.7500   3rd Qu.:0.7500   3rd Qu.:0.7569   3rd Qu.:0.7625  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##                                                                     
##      auc12            auc13            auc14            auc15       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.5066   1st Qu.:0.5066   1st Qu.:0.5066   1st Qu.:0.4000  
##  Median :0.7071   Median :0.7071   Median :0.7071   Median :0.6181  
##  Mean   :0.6636   Mean   :0.6636   Mean   :0.6636   Mean   :0.5889  
##  3rd Qu.:0.8333   3rd Qu.:0.8333   3rd Qu.:0.8333   3rd Qu.:0.8185  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##                                                                     
##       auc         
##  Min.   :0.07751  
##  1st Qu.:0.45509  
##  Median :0.54991  
##  Mean   :0.57606  
##  3rd Qu.:0.70616  
##  Max.   :1.00000  
## 

characteristic_table(dnnsurv1)[[1]]

## [1] 0.8

1.10 gse1

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/gse1_p_cox1.rds")
pcox2=readRDS("savedresults/gse1_p_cox2.rds")
pcox3=readRDS("savedresults/gse1_p_cox3.rds")
rsf1=readRDS("savedresults/gse1_rsf1.rds")
rsf2=readRDS("savedresults/gse1_rsf2.rds")
mtlr1=readRDS("savedresults/gse1_mtlr.rds")
dnnsurv1=empty_list
coxboost=readRDS("savedresults/gse1_coxboost.rds")

gacox=readRDS("savedresults/gse1_ga_cox1.rds")
gamtlr=readRDS("savedresults/gse1_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/gse1_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/gse1_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/gse1_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/gse1_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

gse1full=want[[1]]
g64=want[[2]]
g65=want[[3]]
g66=want[[4]]
g67=want[[5]]
g68=want[[6]]
g69=want[[7]]
g70=want[[8]]

characteristic_table(pcox1)[[2]]

##     model                 hc               bc              unoc       
##  Length:100         Min.   :0.2828   Min.   :0.4326   Min.   :0.0000  
##  Class :character   1st Qu.:0.4914   1st Qu.:0.4960   1st Qu.:0.2035  
##  Mode  :character   Median :0.5216   Median :0.5217   Median :0.4891  
##                     Mean   :0.5263   Mean   :0.5850   Mean   :0.4593  
##                     3rd Qu.:0.5798   3rd Qu.:0.5526   3rd Qu.:0.7034  
##                     Max.   :0.6675   Max.   :1.0000   Max.   :0.9995  
##                                                                       
##       ghc              bs1               bs2               bs3     
##  Min.   :0.0000   Min.   :   36.3   Min.   :   36.3   Min.   : NA  
##  1st Qu.:0.5193   1st Qu.:  166.6   1st Qu.:  166.6   1st Qu.: NA  
##  Median :0.5555   Median :  344.9   Median :  344.9   Median : NA  
##  Mean   :0.4960   Mean   :  830.8   Mean   :  830.8   Mean   :NaN  
##  3rd Qu.:0.6060   3rd Qu.:  930.9   3rd Qu.:  930.9   3rd Qu.: NA  
##  Max.   :0.8125   Max.   :13466.5   Max.   :13466.5   Max.   : NA  
##                                                       NA's   :100  
##       bs4           bs5           bs6           auc1             auc2       
##  Min.   : NA   Min.   : NA   Min.   : NA   Min.   :0.2339   Min.   :0.2230  
##  1st Qu.: NA   1st Qu.: NA   1st Qu.: NA   1st Qu.:0.5000   1st Qu.:0.4991  
##  Median : NA   Median : NA   Median : NA   Median :0.5397   Median :0.5346  
##  Mean   :NaN   Mean   :NaN   Mean   :NaN   Mean   :0.5494   Mean   :0.5422  
##  3rd Qu.: NA   3rd Qu.: NA   3rd Qu.: NA   3rd Qu.:0.6237   3rd Qu.:0.6223  
##  Max.   : NA   Max.   : NA   Max.   : NA   Max.   :0.8539   Max.   :0.8386  
##  NA's   :100   NA's   :100   NA's   :100                                    
##       auc3             auc4             auc5             auc6       
##  Min.   :0.2192   Min.   :0.2192   Min.   :0.2192   Min.   :0.1202  
##  1st Qu.:0.4759   1st Qu.:0.4839   1st Qu.:0.4840   1st Qu.:0.4547  
##  Median :0.5028   Median :0.5089   Median :0.5259   Median :0.5000  
##  Mean   :0.5266   Mean   :0.5410   Mean   :0.5501   Mean   :0.5107  
##  3rd Qu.:0.6022   3rd Qu.:0.6279   3rd Qu.:0.6479   3rd Qu.:0.5779  
##  Max.   :0.7431   Max.   :0.7726   Max.   :0.8464   Max.   :0.7812  
##                                                                     
##       auc7             auc8              auc9            auc10       
##  Min.   :0.1179   Min.   :0.08111   Min.   :0.1401   Min.   :0.1401  
##  1st Qu.:0.4662   1st Qu.:0.44623   1st Qu.:0.4156   1st Qu.:0.4142  
##  Median :0.5000   Median :0.50000   Median :0.5000   Median :0.5000  
##  Mean   :0.5298   Mean   :0.51724   Mean   :0.4984   Mean   :0.4956  
##  3rd Qu.:0.6141   3rd Qu.:0.60407   3rd Qu.:0.5886   3rd Qu.:0.5891  
##  Max.   :0.8143   Max.   :0.80795   Max.   :0.8070   Max.   :0.9260  
##                                                                      
##      auc11             auc12             auc13             auc14        
##  Min.   :0.06721   Min.   :0.07735   Min.   :0.04917   Min.   :0.01518  
##  1st Qu.:0.36094   1st Qu.:0.39947   1st Qu.:0.36693   1st Qu.:0.36549  
##  Median :0.48841   Median :0.50000   Median :0.50000   Median :0.50000  
##  Mean   :0.46363   Mean   :0.48358   Mean   :0.45869   Mean   :0.47460  
##  3rd Qu.:0.51891   3rd Qu.:0.55220   3rd Qu.:0.54834   3rd Qu.:0.55976  
##  Max.   :0.92601   Max.   :0.93106   Max.   :0.93973   Max.   :0.93220  
##                                                                         
##      auc15              auc        
##  Min.   :0.05104   Min.   :0.2182  
##  1st Qu.:0.47657   1st Qu.:0.4880  
##  Median :0.50839   Median :0.5311  
##  Mean   :0.55389   Mean   :0.5264  
##  3rd Qu.:0.69182   3rd Qu.:0.5871  
##  Max.   :0.90737   Max.   :0.7237  
## 

1.11 gse2

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/gse4_p_cox2.rds")
pcox2=readRDS("savedresults/gse4_p_cox2.rds")
pcox3=readRDS("savedresults/gse4_p_cox3.rds")
rsf1=readRDS("savedresults/gse4_rsf1.rds") 
rsf2=readRDS("savedresults/gse4_rsf1.rds") 
#mtlr1=readRDS("savedresults/gse4_mtlr.rds")
mtlr1=empty_list
dnnsurv1=empty_list
coxboost=readRDS("savedresults/gse4_coxboost.rds") 

gacox=readRDS("savedresults/gse4_ga_cox1.rds")
gamtlr=readRDS("savedresults/gse4_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/gse4_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/gse4_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/gse4_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/gse4_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

gse2full=want[[1]]
g71=want[[2]]
g72=want[[3]]
g73=want[[4]]
g74=want[[5]]
g75=want[[6]]
g76=want[[7]]
g77=want[[8]]

characteristic_table(rsf1)[[2]]

##     model                 hc               bc           unoc       
##  Length:100         Min.   :0.1471   Min.   : NA   Min.   :0.0000  
##  Class :character   1st Qu.:0.4808   1st Qu.: NA   1st Qu.:0.0000  
##  Mode  :character   Median :0.5000   Median : NA   Median :0.0000  
##                     Mean   :0.4739   Mean   :NaN   Mean   :0.1718  
##                     3rd Qu.:0.5000   3rd Qu.: NA   3rd Qu.:0.3775  
##                     Max.   :0.7000   Max.   : NA   Max.   :0.7092  
##                                      NA's   :100                   
##       ghc           bs1             bs2             bs3           bs4     
##  Min.   : NA   Min.   :1.333   Min.   :1.333   Min.   : NA   Min.   : NA  
##  1st Qu.: NA   1st Qu.:2.108   1st Qu.:2.108   1st Qu.: NA   1st Qu.: NA  
##  Median : NA   Median :2.569   Median :2.569   Median : NA   Median : NA  
##  Mean   :NaN   Mean   :2.648   Mean   :2.648   Mean   :NaN   Mean   :NaN  
##  3rd Qu.: NA   3rd Qu.:3.022   3rd Qu.:3.022   3rd Qu.: NA   3rd Qu.: NA  
##  Max.   : NA   Max.   :4.360   Max.   :4.360   Max.   : NA   Max.   : NA  
##  NA's   :100                                   NA's   :100   NA's   :100  
##       bs5              bs6              auc1             auc2       
##  Min.   :0.1619   Min.   :0.1619   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.1929   1st Qu.:0.1929   1st Qu.:0.4501   1st Qu.:0.5000  
##  Median :0.2138   Median :0.2138   Median :0.5000   Median :0.5000  
##  Mean   :0.2188   Mean   :0.2188   Mean   :0.4559   Mean   :0.4641  
##  3rd Qu.:0.2338   3rd Qu.:0.2338   3rd Qu.:0.5000   3rd Qu.:0.5000  
##  Max.   :0.3140   Max.   :0.3140   Max.   :0.7500   Max.   :0.8125  
##                                                                     
##       auc3              auc4              auc5             auc6       
##  Min.   :0.05556   Min.   :0.05556   Min.   :0.0625   Min.   :0.0000  
##  1st Qu.:0.50000   1st Qu.:0.50000   1st Qu.:0.4910   1st Qu.:0.4600  
##  Median :0.50000   Median :0.50000   Median :0.5000   Median :0.5000  
##  Mean   :0.48997   Mean   :0.48114   Mean   :0.4593   Mean   :0.4497  
##  3rd Qu.:0.50000   3rd Qu.:0.50000   3rd Qu.:0.5000   3rd Qu.:0.5000  
##  Max.   :0.81250   Max.   :0.81250   Max.   :0.8333   Max.   :0.8333  
##                                                                       
##       auc7             auc8             auc9            auc10       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.4305   1st Qu.:0.4305   1st Qu.:0.4305   1st Qu.:0.4305  
##  Median :0.5000   Median :0.5000   Median :0.5000   Median :0.5000  
##  Mean   :0.4445   Mean   :0.4445   Mean   :0.4445   Mean   :0.4465  
##  3rd Qu.:0.5000   3rd Qu.:0.5000   3rd Qu.:0.5000   3rd Qu.:0.5000  
##  Max.   :0.6764   Max.   :0.6764   Max.   :0.6764   Max.   :0.7500  
##                                                                     
##      auc11            auc12            auc13            auc14       
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.4305   1st Qu.:0.4936   1st Qu.:0.4340   1st Qu.:0.4323  
##  Median :0.5000   Median :0.5000   Median :0.5000   Median :0.5000  
##  Mean   :0.4512   Mean   :0.4667   Mean   :0.4518   Mean   :0.4573  
##  3rd Qu.:0.5000   3rd Qu.:0.5000   3rd Qu.:0.5000   3rd Qu.:0.5000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##                                                                     
##      auc15             auc        
##  Min.   :0.0000   Min.   :0.0795  
##  1st Qu.:0.3932   1st Qu.:0.4435  
##  Median :0.5000   Median :0.5000  
##  Mean   :0.4438   Mean   :0.4588  
##  3rd Qu.:0.5000   3rd Qu.:0.5000  
##  Max.   :1.0000   Max.   :0.6883  
## 

1.12 ngene1

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse1_p_cox1.rds")
pcox2=readRDS("savedresults/ngse1_p_cox2.rds")
pcox3=readRDS("savedresults/ngse1_p_cox3.rds")
rsf1=readRDS("savedresults/ngse1_rsf1.rds") 
rsf2=readRDS("savedresults/ngse1_rsf1.rds") 
mtlr1=readRDS("savedresults/ngse1_mtlr.rds") 
dnnsurv1=readRDS("savedresults/ngse1_dnnsurv.rds") 
coxboost=readRDS("savedresults/ngse1_coxboost.rds") 

gacox=readRDS("savedresults/ngse1_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse1_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse1_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse1_limma_mtlr.rds")
#limmacoxboost=readRDS("NGSE/ngse1_limma_coxboost.rds")
limmacoxboost=empty_list
survivalsvm=readRDS("savedresults/ngse1_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

ngse1full=want[[1]]
g85=want[[2]]
g86=want[[3]]
g87=want[[4]]
g88=want[[5]]
g89=want[[6]]
g90=want[[7]]
g91=want[[8]]

characteristic_table(dnnsurv1)[[1]]

## [1] 0.95

1.13 ngene2

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse2_p_cox1.rds")
pcox2=readRDS("savedresults/ngse2_p_cox2.rds")
pcox3=readRDS("savedresults/ngse2_p_cox3.rds")
rsf1=readRDS("savedresults/ngse2_rsf1.rds") 
rsf2=readRDS("savedresults/ngse2_rsf1.rds") 
mtlr1=readRDS("savedresults/ngse2_mtlr.rds") 
dnnsurv1=readRDS("savedresults/ngse2_dnnsurv.rds") 
coxboost=readRDS("savedresults/ngse2_coxboost.rds") 

gacox=readRDS("savedresults/ngse2_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse2_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse2_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse2_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse2_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/ngse2_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

ngse2full=want[[1]]
g92=want[[2]]
g93=want[[3]]
g94=want[[4]]
g95=want[[5]]
g96=want[[6]]
g97=want[[7]]
g98=want[[8]]
characteristic_table(dnnsurv1)[[1]]

## [1] 0.98

1.14 ngene3

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse3_p_cox1.rds")
pcox2=readRDS("savedresults/ngse3_p_cox2.rds")
pcox3=readRDS("savedresults/ngse3_p_cox3.rds")
rsf1=readRDS("savedresults/ngse3_rsf1.rds") 
rsf2=readRDS("savedresults/ngse3_rsf1.rds") 
mtlr1=readRDS("savedresults/ngse3_mtlr.rds") 
dnnsurv1=readRDS("savedresults/ngse3_dnnsurv.rds") 
coxboost=readRDS("savedresults/ngse3_coxboost.rds") 

gacox=readRDS("savedresults/ngse3_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse3_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse3_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse3_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse3_limma_coxboost.rds")
survivalsvm=empty_list
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

ngse3full=want[[1]]
g99=want[[2]]
g100=want[[3]]
g101=want[[4]]
g102=want[[5]]
g103=want[[6]]
g104=want[[7]]
g105=want[[8]]

characteristic_table(dnnsurv1)[[1]]

## [1] 0.97

1.15 ngene4

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse4_p_cox1.rds")
pcox2=readRDS("savedresults/ngse4_p_cox2.rds")
pcox3=readRDS("savedresults/ngse4_p_cox3.rds")
rsf1=readRDS("savedresults/ngse4_rsf1.rds") 
rsf2=readRDS("savedresults/ngse4_rsf1.rds") 
mtlr1=readRDS("savedresults/ngse4_mtlr.rds") 
dnnsurv1=readRDS("savedresults/ngse4_dnnsurv.rds") 
coxboost=readRDS("savedresults/ngse4_coxboost.rds") 

gacox=readRDS("savedresults/ngse4_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse4_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse4_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse4_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse4_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/ngse4_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

ngse4full=want[[1]]
g106=want[[2]]
g107=want[[3]]
g108=want[[4]]
g109=want[[5]]
g110=want[[6]]
g111=want[[7]]
g112=want[[8]]

characteristic_table(dnnsurv1)[[1]]

## [1] 1

1.16 ngene5

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse5_p_cox1.rds")
pcox2=readRDS("savedresults/ngse5_p_cox2.rds")
pcox3=readRDS("savedresults/ngse5_p_cox3.rds")
rsf1=readRDS("savedresults/ngse5_rsf1.rds") 
rsf2=readRDS("savedresults/ngse5_rsf1.rds") 
mtlr1=readRDS("savedresults/ngse5_mtlr.rds") 
dnnsurv1=readRDS("savedresults/ngse5_dnnsurv.rds") 
coxboost=readRDS("savedresults/ngse5_coxboost.rds") 

gacox=readRDS("savedresults/ngse5_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse5_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse5_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse5_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse5_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/ngse5_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

ngse5full=want[[1]]
g113=want[[2]]
g114=want[[3]]
g115=want[[4]]
g116=want[[5]]
g117=want[[6]]
g118=want[[7]]
g119=want[[8]]
characteristic_table(dnnsurv1)[[1]]

## [1] 1

1.17 ngene6

empty_list=list()
for(i in 1:50){
  empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
  colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4",  "bs5", "bs6" , "auc1"  , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13",  "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse6_p_cox1.rds")
pcox2=readRDS("savedresults/ngse6_p_cox2.rds")
pcox3=readRDS("savedresults/ngse6_p_cox3.rds")
rsf1=readRDS("savedresults/ngse6_rsf1.rds") 
rsf2=readRDS("savedresults/ngse6_rsf1.rds") 
mtlr1=readRDS("savedresults/ngse6_mtlr.rds") 
dnnsurv1=readRDS("savedresults/ngse6_dnnsurv.rds") 
coxboost=readRDS("savedresults/ngse6_coxboost.rds") 

gacox=readRDS("savedresults/ngse6_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse6_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse6_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse6_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse6_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/ngse6_survivalsvm.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm)

ngse6full=want[[1]]
g120=want[[2]]
g121=want[[3]]
g122=want[[4]]
g123=want[[5]]
g124=want[[6]]
g125=want[[7]]
g126=want[[8]]

characteristic_table(dnnsurv1)[[1]]

## [1] 1

2 each plot

labelss<-c("pbc","veteran","lung","anz","us","melanoma_clinical","melanoma_itraq","melanoma_swath","melanoma_nano","gse1","gse2","gene1","gene2","gene3","gene4","gene5","gene6")
ggarrange(g1,g8,g15,g22,g29,g36,g43,g50,g57,g64,g71,g85,g92,g99,g106,g113,g120,labels = labelss,ncol = 3, nrow = 6) #miss g78 for gse3

ggarrange(g6,g13, g20,g27,g34,g41,g48,g55,g62,g69,g76,g90,g97,g104,g111,g118,g125,labels = labelss,ncol = 3, nrow = 6) #g83

ggarrange(g5,g12, g19,g26,g33,g40,g47,g54,g61,g68,g75,g89,g96,g103,g110,g117,g124,labels = labelss,ncol = 3, nrow = 6) #g82

# ggarrange(g2,g9, g16,g23,g30,g35,g44,g51,g58,g65,g72,g79,labels = labelss,ncol = 3, nrow = 4)
# ggarrange(g4,g11, g18,g25,g32,g37,g46,g53,g60,g67,g74,g81,labels = labelss,ncol = 3, nrow = 4)

3 combined plot

3.1 data summary

cen=read_excel("result.xlsx",sheet = 2)
kable(cen)

Data	Size (observations, features)	Censoring rate
ANZDATA	(3323, 40)	0.8739
US	(3000, 103)	0.7350
veteran	(137, 8)	0.0657
lung	(228, 10)	0.2763
melanomaitraq	(41, 310)	0.4146
melanomaswath	(70, 1629)	0.4571
melanomaclinical	(88, 23)	0.3939
melanomanano	(45, 206)	0.4222
pbc	(312, 7)	0.5994
gse1	(194, 16050)	0.7062
gse2	(58, 19818)	0.3793
ngene1	(115, 549)	0.6670
ngene2	(295, 4919)	0.7322
ngene3	(86, 7129)	0.7209
ngene4	(116, 4751)	0.5641
ngene5	(78, 4751)	0.5641
ngene6	(240, 7399)	0.4250

3.2 feasibility summary

plotdt=read_excel("result.xlsx",sheet = 1)
plotdt=as.data.frame(plotdt)
rownames(plotdt)=plotdt$method
#pheatmap(as.matrix(plotdt[,-1]),cluster_rows = FALSE,cluster_cols = TRUE)
col_fun = colorRamp2(c( 0, 1), c("seagreen", "lightpink"))
mat=as.matrix(plotdt[,-1])
Heatmap(mat, name = "index", col = col_fun,cluster_rows = FALSE,rect_gp = gpar(col = "white", lwd = 2))

plotdt=read_excel("result.xlsx",sheet = 7)
plotdt=as.data.frame(plotdt)
rownames(plotdt)=plotdt$method
#pheatmap(as.matrix(plotdt[,-1]),cluster_rows = FALSE,cluster_cols = TRUE)
col_fun = colorRamp2(c( 0, 1), c("seagreen", "lightpink"))
mat=as.matrix(plotdt[,-1])
Heatmap(mat, name = "index", col = col_fun,cluster_rows = FALSE,rect_gp = gpar(col = "white", lwd = 2))

3.3 clinical vs omics

data_list=list(anzfull,usfull,veteranfull,lungfull,pbcfull,melanoma_clinicalfull,melanoma_itraqfull,melanoma_swathfull,melanomananofull,gse1full,gse2full,ngse1full,ngse2full,ngse3full,ngse4full,ngse5full,ngse6full)
datafull=bind_rows(data_list, .id = "datasets")

nb.cols <- 17
mycolors <- colorRampPalette(brewer.pal(8, "RdBu"))(nb.cols)

plotdt=datafull[datafull$model=="rsf2",]
plotdt=plotdt[plotdt$variable=="bs5",]
plotdt2=plotdt[!is.infinite(plotdt$value),]
#plotdt2$observations=as.numeric(mapvalues(plotdt2$datasets, from = seq(1,11,1), to = c(3323,3000,137,228,312,88,41,70,45,194,58)))
#plotdt2=plotdt2 %>% arrange(observations)
#plotdt$datasets=factor(plotdt$datasets,levels=c("anzfull","usfull","veteranfull","lungfull","ovarianfull","melanoma_clinicalfull","melanoma_itraqfull","melanoma_swathfull","ovarian1full","ovarian2full"))
plotdt2$datasets=mapvalues(plotdt2$datasets, from = seq(1,17,1), to = c("anz3323","us3000","veteran137","lung228","pbc312","melanoma_clinical88","melanoma_itraq41","melanoma_swath70","melanoma_nano45","gse1 194","gse2 58","ngene1 115","ngene2 295","mgene3 86","ngene4 116","ngene5 78","ngene6 240"))
plotdt2$datasets=factor(plotdt2$datasets,levels=c("melanoma_itraq41","melanoma_nano45","gse2 58","melanoma_swath70","ngene5 78","mgene3 86","ngene1 115","ngene4 116","gse1 194","ngene6 240","ngene2 295","melanoma_clinical88","veteran137","lung228","pbc312","us3000","anz3323"))
p=ggplot(plotdt2, aes(x=datasets, y=value, fill=datasets)) + geom_boxplot()+scale_y_continuous(limits=c(0,0.25))+ scale_fill_manual(values = mycolors)
p

plotdt=datafull[datafull$model=="coxboost",]
plotdt=plotdt[plotdt$variable=="hc",]
plotdt2=plotdt[!is.infinite(plotdt$value),]

#plotdt$datasets=factor(plotdt$datasets,levels=c("anzfull","usfull","veteranfull","lungfull","ovarianfull","melanoma_clinicalfull","melanoma_itraqfull","melanoma_swathfull","ovarian1full","ovarian2full"))
plotdt2$datasets=mapvalues(plotdt2$datasets, from = seq(1,17,1), to = c("anz3323","us3000","veteran137","lung228","pbc312","melanoma_clinical88","melanoma_itraq41","melanoma_swath70","melanoma_nano45","gse1 194","gse2 58","ngene1 115","ngene2 295","mgene3 86","ngene4 116","ngene5 78","ngene6 240"))
plotdt2$datasets=factor(plotdt2$datasets,levels=c("melanoma_itraq41","melanoma_nano45","gse2 58","melanoma_swath70","ngene5 78","mgene3 86","ngene1 115","ngene4 116","gse1 194","ngene6 240","ngene2 295","melanoma_clinical88","veteran137","lung228","pbc312","us3000","anz3323"))
p=ggplot(plotdt2, aes(x=datasets, y=value, fill=datasets)) + geom_boxplot()+scale_y_continuous(limits=c(0,1))+ scale_fill_manual(values = mycolors)
p

3.4 mean hc (data*method)

plotdt=datafull[datafull$variable=="hc",]
plotdt2 <- plotdt %>%dplyr::group_by(datasets,model) %>%dplyr::summarize(Mean = mean(value, na.rm=TRUE))
plotdt2$datasets=plyr::mapvalues(plotdt2$datasets, from = seq(1,17,1), to = c("anz","us","veteran","lung","pbc","melanoma_clinical","melanoma_itraq","melanoma_swath","melanoma_nano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
plotdt2$datasets=factor(plotdt2$datasets,levels=c("anz","us","veteran","lung","pbc","melanoma_clinical","melanoma_itraq","melanoma_swath","melanoma_nano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
mat=tidyr::spread(plotdt2, model, Mean)
mat=as.data.frame(mat)
rownames(mat)=mat$datasets
mat=as.matrix(mat[,-1])
Heatmap(mat, name = "index", cluster_rows = FALSE,cluster_columns = FALSE,rect_gp = gpar(col = "white", lwd = 2))

3.5 performance (model*metric, average over datasets)

plotaccuracy=datafull[!(datafull$variable %in% c("bs1","bs2","bs3","bs4","bs5","bs6")),]
plotdtt=plotaccuracy%>% group_by(model,variable) %>%dplyr::summarize(Mean = mean(value, na.rm=TRUE))
mat=tidyr::spread(plotdtt, model, Mean)
mat=as.data.frame(mat)
mat=do.call(data.frame,lapply(mat, function(x) replace(x, is.infinite(x),NA)))
rownames(mat)=c("Harrell C","Begg C","Uno C","GH C","AUC1","AUC2","AUC3","AUC4","AUC5","AUC6","AUC7","AUC8","AUC9","AUC10","AUC11","AUC12","AUC13","AUC14","AUC15","integrated AUC")
colnames(mat)=c("variable","cox","cox+bwAIC","cox+bwp","cox+bwBIC","coxboost","dnnsurv","gacox","gacoxboost","gamtlr","limmacoxboost","limmamtlr","mtlr","LassoCox","RidgeCox","ElasticNetCox","RSF1","RSF2","survivalsvm")
mat=as.matrix(mat[,-1])
col_fun = colorRamp2(c( 0,0.5,1), c("seagreen", "grey","lightpink"))
Heatmap(mat, name = "index", col = col_fun,cluster_rows = FALSE,cluster_columns = FALSE,rect_gp = gpar(col = "white", lwd = 2))

plotaccuracy=datafull[(datafull$variable %in% c("bs1","bs2")),]
plotdtt=plotaccuracy%>% group_by(model,variable) %>%dplyr::summarize(Mean = mean(value, na.rm=TRUE))
mat=tidyr::spread(plotdtt, model, Mean)
mat=as.data.frame(mat)
mat=do.call(data.frame,lapply(mat, function(x) replace(x, is.infinite(x),NA)))
rownames(mat)=c("Brier score 1","Brier score 2")
colnames(mat)=c("variable","cox","cox+bwAIC","cox+bwp","cox+bwBIC","coxboost","dnnsurv","gacox","gacoxboost","gamtlr","limmacoxboost","limmamtlr","mtlr","LassoCox","RidgeCox","ElasticNetCox","RSF1","RSF2","survivalsvm")
mat=as.matrix(mat[,-1])

Heatmap(mat, name = "index",cluster_rows = FALSE,cluster_columns = FALSE,rect_gp = gpar(col = "white", lwd = 2))

3.6 some picked comparison plots

#1:anz
#16:ngene5
plotdt=datafull[datafull$model%in% c("cox1","cox2","cox3","pcox1","pcox2","pcox3","coxboost"),]
plotdt=plotdt[plotdt$variable=="hc"& plotdt$datasets==1,]
plotdt2=plotdt[!is.infinite(plotdt$value),]
ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()

plotdt=datafull[datafull$model%in% c("cox1","cox2","cox3","pcox1","pcox2","pcox3","coxboost"),]
plotdt=plotdt[plotdt$variable=="bs1"& plotdt$datasets==1,]
plotdt2=plotdt[!is.infinite(plotdt$value),]
ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()

plotdt=datafull[datafull$model%in% c("pcox1","coxboost","dnnsurv1","limmacoxboost"),]
plotdt=plotdt[plotdt$variable=="hc"& plotdt$datasets==16,]
plotdt2=plotdt[!is.infinite(plotdt$value),]
ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()

plotdt=datafull[datafull$model%in% c("pcox1","coxboost","dnnsurv1","limmacoxboost"),]
plotdt=plotdt[plotdt$variable=="bs1"& plotdt$datasets==16,]
plotdt2=plotdt[!is.infinite(plotdt$value),]
ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()

# plotdt=datafull[datafull$model%in% c("cox1","cox2","cox3","pcox1","pcox2","pcox3","coxboost","dnnsurv1"),]
# plotdt=plotdt[plotdt$variable=="hc"& plotdt$datasets==3,]
# plotdt2=plotdt[!is.infinite(plotdt$value),]
# ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()
# plotdt=datafull[datafull$model%in% c("cox1","cox2","cox3","pcox1","pcox2","pcox3","coxboost","dnnsurv1"),]
# plotdt=plotdt[plotdt$variable=="hc"& plotdt$datasets==4,]
# plotdt2=plotdt[!is.infinite(plotdt$value),]
# ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()
# plotdt=datafull[datafull$model%in% c("cox1","cox2","cox3","pcox1","pcox2","pcox3","coxboost","dnnsurv1"),]
# plotdt=plotdt[plotdt$variable=="hc"& plotdt$datasets==5,]
# plotdt2=plotdt[!is.infinite(plotdt$value),]
# ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()

3.7 reproducibility

plottdt=data.frame(method=rep(c("bw_aic","bw_p","bw_bic"),2),datasets=rep(c("m_itraq","m_nano"),each=3),failure=c(15,30,10,75,90,55))

ggplot(data = plottdt, aes(x = "", y = failure, fill = method )) + 
    geom_bar(stat = "identity", position = position_fill()) +
    geom_text(aes(label = failure), position = position_fill(vjust = 0.5)) +
    coord_polar(theta = "y") +
    facet_wrap(~ datasets)  +
    theme(axis.title.x = element_blank(),
          axis.title.y = element_blank()) + 
    theme(legend.position='bottom') + 
    guides(fill=guide_legend(nrow=2, byrow=TRUE))

plottdt=read_excel("result.xlsx",sheet = 8)
plottdt=plottdt[11,]
plottdt=as.data.frame(t(plottdt))
plottdt$data=rownames(plottdt)
plottdt=plottdt[which(plottdt$V1>0),]
ggplot(data = plottdt[-1,], aes(x = "", y = V1, fill = data )) + geom_bar(stat = "identity", position = position_dodge())

plottdt=data.frame(method=c("bw_coxaic","bw_coxp","bw_coxbic"),value=c(0.75,0.9,0.55))
ggplot(data = plottdt, aes(x = "", y = value, fill = method )) + geom_bar(stat = "identity", position = position_fill())+ coord_polar(theta = "y") +
    geom_text(aes(label = value), position = position_fill(vjust = 0.5))+
    theme(legend.position='bottom') + 
    guides(fill=guide_legend(nrow=1, byrow=TRUE))

4 supplymentary

4.1 linear model on cindex

check linear model: which aspect affects cindex

regressiondt=datafull%>% filter(variable=="hc")%>% group_by(datasets,model)%>% dplyr::summarise(medianc=median(value))
regressiondt$datasets=mapvalues(regressiondt$datasets, from = seq(1,11,1), to = c("anz","us","veteran","lung","pbc","melanoma_clinical","melanoma_itraq","melanoma_swath","melanoma_nano","gse1","gse2"))
tablee=read_excel("result.xlsx",sheet = 9)
regressiondt1 <- tablee %>% left_join(regressiondt, by = c("name" = "datasets"))
regressiondt2<-regressiondt1%>% distinct()

fitted=lm(medianc~.,data = regressiondt2[,-1])
summary(fitted)
head(regressiondt2)
stargazer(fitted, type = "text")

heatmap of cindex for model vs data

olddata_long=regressiondt2[,c(1,7,8)]
data_wide <- spread(olddata_long,model, medianc)
data_wide
plotdt=as.data.frame(data_wide)
rownames(plotdt)=plotdt$name
#pheatmap(as.matrix(plotdt[,-1]),cluster_rows = FALSE,cluster_cols = TRUE)
col_fun = colorRamp2(c( 0, 0.5,1), c("grey","seagreen", "lightpink"))
mat=as.matrix(plotdt[,-1])
mat[is.na(mat)] <- 0
Heatmap(mat, name = "index", col = col_fun,cluster_rows = FALSE,cluster_columns = FALSE,rect_gp = gpar(col = "white", lwd = 2))

check for this data pbc from another package: still, mtlr is bad

data("pbc",package = "survival")
dim(pbc)
colnames(pbc)

pbc1 <- pbc[,-1]
dim(pbc1)
any(is.na(pbc1))
pbc2=na.omit(pbc1)
dim(pbc2) #276 19
table(pbc2$status) 
pbc3=pbc2[pbc2$status!=1,]
pbc3$status=as.vector(ifelse(pbc3$status==2,1,0))
table(pbc3$status) #147 111
fitform_ogl=as.formula(Surv(time, status) ~ .)
summary(pbc3$time)

formula1=fitform_ogl
formula2=fitform_ogl
formula3=Surv(time,status)~1
formula4=Surv(time,status)~1
timess=seq(50,2000,(2000-50)/15)
#9

cox1 <- pbmcapply::pbmclapply(1:20, rsf2_fun,pbc3,5, fitform_ogl,formula1,formula2,formula3,formula4,timess, mc.cores = 2)

head(cox1)
colMeans(do.call(rbind, cox1))
#10

cox2 <- pbmcapply::pbmclapply(1:20, mtlr_fun,pbc3,5, fitform_ogl,formula1,formula2,formula3,formula4,timess, mc.cores = 2)

head(cox2)
colMeans(do.call(rbind, cox2))

4.2 time and memory full report

time_fun=function(model_list){
  time_vec=c()
for (i in 1:18){
  dt=model_list[[i]]
  if (is.null(dt)){time_vec[i]=NA} else {time_vec[i]=dt}
}
  return(time_vec)
}


memory_fun=function(model_list){
  memory_vec=c()
for (i in 1:18){
  dt=model_list[[i]]
  if (is.null(dt)){memory_vec[i]=NA} else {memory_vec[i]=sum(dt$by.total$mem.total)}
}
  return(memory_vec)
}

plot_fun1=function(valuee){
  df=data.frame(methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm"), value=valuee)
  p<-ggplot(data=df, aes(x=methods, y=value)) +
  geom_bar(stat="identity")+ggtitle("               memory(MB)")
  return(p)
}

plot_fun2=function(valuee){
  df=data.frame(methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm"), value=valuee)
  p<-ggplot(data=df, aes(x=methods, y=value)) +
  geom_bar(stat="identity")+ggtitle("                time(seconds)")
  return(p)
}

#pbc
cox1=readRDS("savedresults/pbc_cox1m.rds")
cox2=readRDS("savedresults/pbc_bw_cox1m.rds")
cox3=readRDS("savedresults/pbc_bw_cox2m.rds")
cox4=readRDS("savedresults/pbc_bw_cox3m.rds")
pcox1=readRDS("savedresults/pbc_p_cox1m.rds")
pcox2=readRDS("savedresults/pbc_p_cox2m.rds")
pcox3=readRDS("savedresults/pbc_p_cox3m.rds")
rsf1=readRDS("savedresults/pbc_rsf1m.rds")
rsf2=readRDS("savedresults/pbc_rsf2m.rds")
mtlr1=readRDS("savedresults/pbc_mtlrm.rds")
dnnsurv1=readRDS("savedresults/pbc_dnnsurvm.rds")
coxboost=readRDS("savedresults/pbc_coxboostm.rds")
survivalsvm=readRDS("savedresults/pbc_survivalsvmm.rds")
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=NULL
limma_coxboost=NULL
survivalsvm=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=readRDS("savedresults/pbc_cox1t.rds")
tcox2=readRDS("savedresults/pbc_bw_cox1t.rds")
tcox3=readRDS("savedresults/pbc_bw_cox2t.rds")
tcox4=readRDS("savedresults/pbc_bw_cox3t.rds")
tpcox1=readRDS("savedresults/pbc_p_cox1t.rds")
tpcox2=readRDS("savedresults/pbc_p_cox2t.rds")
tpcox3=readRDS("savedresults/pbc_p_cox3t.rds")
trsf1=readRDS("savedresults/pbc_rsf1t.rds")
trsf2=readRDS("savedresults/pbc_rsf2t.rds")
tmtlr1=readRDS("savedresults/pbc_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/pbc_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/pbc_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/pbc_survivalsvmt.rds")*60
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=NULL
tlimma_coxboost=NULL
tsurvivalsvm=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g1=plot_fun1(m)
g2=plot_fun2(t)
pbcall=cbind.data.frame(m,t,m1,t1)
pbcall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#veteran
cox1=readRDS("savedresults/veteran_cox1m.rds")
cox2=readRDS("savedresults/veteran_bw_cox1m.rds")
cox3=readRDS("savedresults/veteran_bw_cox2m.rds")
cox4=readRDS("savedresults/veteran_bw_cox3m.rds")
pcox1=readRDS("savedresults/veteran_p_cox1m.rds")
pcox2=readRDS("savedresults/veteran_p_cox2m.rds")
pcox3=readRDS("savedresults/veteran_p_cox3m.rds")
rsf1=readRDS("savedresults/veteran_rsf1m.rds")
rsf2=readRDS("savedresults/veteran_rsf2m.rds")
mtlr1=readRDS("savedresults/veteran_mtlrm.rds")
dnnsurv1=readRDS("savedresults/veteran_dnnsurvm.rds")
coxboost=readRDS("savedresults/veteran_coxboostm.rds")
survivalsvm=readRDS("savedresults/veteran_survivalsvmm.rds")
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=NULL
limma_coxboost=NULL
survivalsvm=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=readRDS("savedresults/veteran_cox1t.rds")
tcox2=readRDS("savedresults/veteran_bw_cox1t.rds")
tcox3=readRDS("savedresults/veteran_bw_cox2t.rds")
tcox4=readRDS("savedresults/veteran_bw_cox3t.rds")
tpcox1=readRDS("savedresults/veteran_p_cox1t.rds")
tpcox2=readRDS("savedresults/veteran_p_cox2t.rds")
tpcox3=readRDS("savedresults/veteran_p_cox3t.rds")
trsf1=readRDS("savedresults/veteran_rsf1t.rds")
trsf2=readRDS("savedresults/veteran_rsf2t.rds")
tmtlr1=readRDS("savedresults/veteran_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/veteran_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/veteran_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/veteran_survivalsvmt.rds")
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=NULL
tlimma_coxboost=NULL
tsurvivalsvm=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g3=plot_fun1(m)
g4=plot_fun2(t)
veteranall=cbind.data.frame(m,t,m1,t1)
veteranall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#lung
cox1=readRDS("savedresults/lung_cox1m.rds")
cox2=readRDS("savedresults/lung_bw_cox1m.rds")
cox3=readRDS("savedresults/lung_bw_cox2m.rds")
pcox1=readRDS("savedresults/lung_p_cox1m.rds")
pcox2=readRDS("savedresults/lung_p_cox2m.rds")
pcox3=readRDS("savedresults/lung_p_cox3m.rds")
rsf1=readRDS("savedresults/lung_rsf1m.rds")
rsf2=readRDS("savedresults/lung_rsf2m.rds")
mtlr1=readRDS("savedresults/lung_mtlrm.rds")
dnnsurv1=readRDS("savedresults/lung_dnnsurvm.rds")
coxboost=readRDS("savedresults/lung_coxboostm.rds")
cox4=readRDS("savedresults/lung_bw_cox3m.rds")
survivalsvm=readRDS("savedresults/lung_survivalsvmm.rds")
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=NULL
limma_coxboost=NULL
survivalsvm=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=readRDS("savedresults/lung_cox1t.rds")
tcox2=readRDS("savedresults/lung_bw_cox1t.rds")
tcox3=readRDS("savedresults/lung_bw_cox2t.rds")
tpcox1=readRDS("savedresults/lung_p_cox1t.rds")
tpcox2=readRDS("savedresults/lung_p_cox2t.rds")
tpcox3=readRDS("savedresults/lung_p_cox3t.rds")
trsf1=readRDS("savedresults/lung_rsf1t.rds")
trsf2=readRDS("savedresults/lung_rsf2t.rds")
tmtlr1=readRDS("savedresults/lung_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/lung_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/lung_coxboostt.rds")
tcox4=readRDS("savedresults/lung_bw_cox3t.rds")
tsurvivalsvm=readRDS("savedresults/lung_survivalsvmt.rds")
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=NULL
tlimma_coxboost=NULL
tsurvivalsvm=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g5=plot_fun1(m)
g6=plot_fun2(t)
lungall=cbind.data.frame(m,t,m1,t1)
lungall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")

#anz
cox1=readRDS("savedresults/anz_cox1m.rds")
cox2=readRDS("savedresults/anz_bw_cox1m.rds")
cox3=readRDS("savedresults/anz_bw_cox2m.rds")
cox4=readRDS("savedresults/anz_bw_cox3m.rds")
pcox1=readRDS("savedresults/anz_p_cox1m.rds")
pcox2=readRDS("savedresults/anz_p_cox2m.rds")
pcox3=readRDS("savedresults/anz_p_cox3m.rds")
rsf1=readRDS("savedresults/anz_rsf1m.rds")
rsf2=readRDS("savedresults/anz_rsf2m.rds")
mtlr1=readRDS("savedresults/anz_mtlrm.rds")
dnnsurv1=readRDS("savedresults/anz_dnnsurvm.rds")
coxboost=readRDS("savedresults/anz_coxboostm.rds")
survivalsvm=NULL
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=NULL
limma_coxboost=NULL
survivalsvm=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=readRDS("savedresults/anz_cox1t.rds")
tcox2=readRDS("savedresults/anz_bw_cox1t.rds")
tcox3=readRDS("savedresults/anz_bw_cox2t.rds")
tcox4=readRDS("savedresults/anz_bw_cox3t.rds")
tpcox1=readRDS("savedresults/anz_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/anz_p_cox2t.rds")
tpcox3=readRDS("savedresults/anz_p_cox3t.rds")*60
trsf1=readRDS("savedresults/anz_rsf1t.rds")*60
trsf2=readRDS("savedresults/anz_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/anz_mtlrt.rds")*60
tdnnsurv1=readRDS("savedresults/anz_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/anz_coxboostt.rds")
tsurvivalsvm=NULL
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=NULL
tlimma_coxboost=NULL
tsurvivalsvm=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g7=plot_fun1(m)
g8=plot_fun2(t)
anzall=cbind.data.frame(m,t,m1,t1)
anzall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#us
cox1=readRDS("savedresults/us_cox1m.rds")
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/us_p_cox1m.rds")
pcox2=readRDS("savedresults/us_p_cox2m.rds")
pcox3=readRDS("savedresults/us_p_cox3m.rds")
rsf1=readRDS("savedresults/us_rsf1m.rds")
rsf2=readRDS("savedresults/us_rsf2m.rds")
mtlr1=readRDS("savedresults/us_mtlrm.rds")
dnnsurv1=NULL
coxboost=readRDS("savedresults/us_coxboostm.rds")
survivalsvm=NULL
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=NULL
limma_coxboost=NULL
survivalsvm=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=readRDS("savedresults/us_cox1t.rds")
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/us_p_cox1t.rds")*3600
tpcox2=readRDS("savedresults/us_p_cox2t.rds")
tpcox3=readRDS("savedresults/us_p_cox3t.rds")*3600
trsf1=readRDS("savedresults/us_rsf1t.rds")*3600
trsf2=readRDS("savedresults/us_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/us_mtlrt.rds")*3600
tdnnsurv1=NULL
tcoxboost=readRDS("savedresults/us_coxboostt.rds")
tsurvivalsvm=NULL
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=NULL
tlimma_coxboost=NULL
tsurvivalsvm=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g9=plot_fun1(m)
g10=plot_fun2(t)
usall=cbind.data.frame(m,t,m1,t1)
usall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#melanomaclinical

cox1=readRDS("savedresults/melanomaclinical_cox1m.rds")
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/melanomaclinical_p_cox1m.rds")
pcox2=readRDS("savedresults/melanomaclinical_p_cox2m.rds")
pcox3=readRDS("savedresults/melanomaclinical_p_cox3m.rds")
rsf1=readRDS("savedresults/melanomaclinical_rsf1m.rds")
rsf2=readRDS("savedresults/melanomaclinical_rsf2m.rds")
mtlr1=readRDS("savedresults/melanomaclinical_mtlrm.rds")
dnnsurv1=readRDS("savedresults/melanomaclinical_dnnsurvm.rds")
coxboost=readRDS("savedresults/melanomaclinica_coxboostm.rds")
survivalsvm=NULL
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=NULL
limma_coxboost=NULL
survivalsvm=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=readRDS("savedresults/melanomaclinical_cox1t.rds")
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/melanomaclinical_p_cox1t.rds")
tpcox2=readRDS("savedresults/melanomaclinical_p_cox2t.rds")
tpcox3=readRDS("savedresults/melanomaclinical_p_cox3t.rds")
trsf1=readRDS("savedresults/melanomaclinical_rsf1t.rds")*60
trsf2=readRDS("savedresults/melanomaclinical_rsf2t.rds")
tmtlr1=readRDS("savedresults/melanomaclinical_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/melanomaclinical_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/melanomaclinica_coxboostt.rds")
tsurvivalsvm=NULL
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=NULL
tlimma_coxboost=NULL
tsurvivalsvm=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)


m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g11=plot_fun1(m)
g12=plot_fun2(t)
melanomaclinicalall=cbind.data.frame(m,t,m1,t1)
melanomaclinicalall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")

#melanomaitraq
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/melanomaitraq_p_cox1m.rds")
pcox2=NULL
pcox3=NULL
rsf1=readRDS("savedresults/melanomaitraq_rsf1m.rds")
rsf2=readRDS("savedresults/melanomaitraq_rsf2m.rds")
mtlr1=readRDS("savedresults/melanomaitraq_mtlrm.rds")
dnnsurv1=readRDS("savedresults/melanomaitraq_dnnsurvm.rds")
coxboost=readRDS("savedresults/melanomaitraq_coxboostm.rds")
ga_cox=readRDS("savedresults/itraq_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/melanomaitraq_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/melanomaitraq_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/melanomaitraq_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/melanomaitraq_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/itraq_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/melanomaitraq_p_cox1t.rds")
tpcox2=NULL
tpcox3=NULL
trsf1=readRDS("savedresults/melanomaitraq_rsf1t.rds")
trsf2=readRDS("savedresults/melanomaitraq_rsf2t.rds")
tmtlr1=readRDS("savedresults/melanomaitraq_mtlrt.rds")*60
tdnnsurv1=readRDS("savedresults/melanomaitraq_dnnsurvt.rds")
tcoxboost=readRDS("savedresults/melanomaitraq_coxboostt.rds")
tga_cox=readRDS("savedresults/itraq_ga_cox1t.rds")
tga_mtlr=readRDS("savedresults/melanomaitraq_ga_mtlrt.rds")
tga_coxboost=readRDS("savedresults/melanomaitraq_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/melanomaitraq_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/melanomaitraq_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/itraq_survivalsvmt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g13=plot_fun1(m)
g14=plot_fun2(t)
melanomaitraqall=cbind.data.frame(m,t,m1,t1)
melanomaitraqall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#melanomaswath
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/melanomaswath_p_cox1m.rds")
pcox2=NULL
pcox3=NULL
rsf1=readRDS("savedresults/melanomaswath_rsf1m.rds")
rsf2=readRDS("savedresults/melanomaswath_rsf2m.rds")
mtlr1=readRDS("savedresults/melanomaswath_mtlrm.rds")
dnnsurv1=NULL
coxboost=readRDS("savedresults/melanomaswath_coxboostm.rds")
ga_cox=readRDS("savedresults/melanomaswath_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/melanomaswath_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/melanomaswath_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/melanomaswath_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/melanomaswath_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/melanomaswath_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/melanomaswath_p_cox1t.rds")
tpcox2=NULL
tpcox3=NULL
trsf1=readRDS("savedresults/lmelanomaswath_rsf1t.rds")*60
trsf2=readRDS("savedresults/melanomaswath_rsf2t.rds")
tmtlr1=readRDS("savedresults/melanomaswath_mtlrt.rds")*60
tdnnsurv1=NULL
tcoxboost=readRDS("savedresults/melanomaswath_coxboostt.rds")
tga_cox=readRDS("savedresults/melanomaswath_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/melanomaswath_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/melanomaswath_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/melanomaswath_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/melanomaswath_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/melanomaswath_survivalsvmt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g15=plot_fun1(m)
g16=plot_fun2(t)
melanomaswathall=cbind.data.frame(m,t,m1,t1)
melanomaswathall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#melanomanano
cox1=NULL
cox2=readRDS("savedresults/melanomanano_bw_cox1m.rds")
cox3=readRDS("savedresults/melanomanano_bw_cox2m.rds")
cox4=readRDS("savedresults/melanomanano_bw_cox3m.rds")
pcox1=readRDS("savedresults/melanomanano_p_cox1m.rds")
pcox2=readRDS("savedresults/melanomanano_p_cox2m.rds")
pcox3=readRDS("savedresults/melanomanano_p_cox3m.rds")
rsf1=readRDS("savedresults/melanomanano_rsf1m.rds")
rsf2=readRDS("savedresults/melanomanano_rsf2m.rds")
mtlr1=readRDS("savedresults/melanomanano_mtlrm.rds")
dnnsurv1=readRDS("savedresults/melanomanano_dnnsurvm.rds")
coxboost=readRDS("savedresults/melanomanano_coxboostm.rds")

survivalsvm=NULL
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=NULL
limma_coxboost=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=readRDS("savedresults/melanomanano_bw_cox1t.rds")
tcox3=readRDS("savedresults/melanomanano_bw_cox2t.rds")
tcox4=readRDS("savedresults/melanomanano_bw_cox3t.rds")
tpcox1=readRDS("savedresults/melanomanano_p_cox1t.rds")
tpcox2=readRDS("savedresults/melanomanano_p_cox2t.rds")
tpcox3=readRDS("savedresults/melanomanano_p_cox3t.rds")
trsf1=readRDS("savedresults/melanomanano_rsf1t.rds")
trsf2=readRDS("savedresults/melanomanano_rsf2t.rds")
tmtlr1=readRDS("savedresults/melanomanano_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/melanomanano_dnnsurvt.rds")
tcoxboost=readRDS("savedresults/melanomanano_coxboostt.rds")

tsurvivalsvm=NULL
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=NULL
tlimma_coxboost=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g17=plot_fun1(m)
g18=plot_fun2(t)
melanomananoall=cbind.data.frame(m,t,m1,t1)
melanomananoall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#gse1
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/gse1_p_cox1m.rds")
pcox2=readRDS("savedresults/gse1_p_cox2m.rds")
pcox3=readRDS("savedresults/gse1_p_cox3m.rds")
rsf1=readRDS("savedresults/gse1_rsf1m.rds")
rsf2=readRDS("savedresults/gse1_rsf2m.rds")
mtlr1=NULL
dnnsurv1=NULL
coxboost=readRDS("savedresults/gse1_coxboostm.rds")
ga_cox=readRDS("savedresults/gse1_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/gse1_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/gse1_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/gse1_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/gse1_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/gse1_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/gse1_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/gse1_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/gse1_p_cox3t.rds")*60
trsf1=readRDS("savedresults/gse1_rsf1t.rds")*60
trsf2=readRDS("savedresults/gse1_rsf2t.rds")*60
tmtlr1=NULL
tdnnsurv1=NULL
tcoxboost=readRDS("savedresults/gse1_coxboostt.rds")*60
tga_cox=readRDS("savedresults/gse1_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/gse1_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/gse1_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/gse1_limma_mtlrt.rds")*60
tlimma_coxboost=readRDS("savedresults/gse1_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/gse1_survivalsvmt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g19=plot_fun1(m)
g20=plot_fun2(t)
gse1all=cbind.data.frame(m,t,m1,t1)
gse1all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#gse2
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/gse4_p_cox1m.rds")
pcox2=readRDS("savedresults/gse4_p_cox2m.rds")
pcox3=readRDS("savedresults/gse4_p_cox3m.rds")
rsf1=readRDS("savedresults/gse4_rsf1m.rds")
rsf2=readRDS("savedresults/gse4_rsf2m.rds")
mtlr1=NULL
dnnsurv1=NULL
coxboost=readRDS("savedresults/gse4_coxboostm.rds")
ga_cox=readRDS("savedresults/gse4_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/gse4_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/gse4_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/gse4_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/gse4_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/gse4_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/gse4_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/gse4_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/gse4_p_cox3t.rds")*60
trsf1=readRDS("savedresults/gse4_rsf1t.rds")*60
trsf2=readRDS("savedresults/gse4_rsf2t.rds")*60
tmtlr1=NULL
tdnnsurv1=NULL
tcoxboost=readRDS("savedresults/gse4_coxboostt.rds")
tga_cox=readRDS("savedresults/gse4_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/gse4_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/gse4_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/gse4_limma_mtlrt.rds")*60
tlimma_coxboost=readRDS("savedresults/gse4_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/gse4_survivalsvmt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g21=plot_fun1(m)
g22=plot_fun2(t)
gse2all=cbind.data.frame(m,t,m1,t1)
gse2all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#ngene1
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse1_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse1_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse1_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse1_rsf1m.rds")
rsf2=readRDS("savedresults/ngse1_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse1_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse1_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse1_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse1_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse1_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse1_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse1_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse1_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse1_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse1_p_cox1t.rds")
tpcox2=readRDS("savedresults/ngse1_p_cox2t.rds")
tpcox3=readRDS("savedresults/ngse1_p_cox3t.rds")
trsf1=readRDS("savedresults/ngse1_rsf1t.rds")
trsf2=readRDS("savedresults/ngse1_rsf2t.rds")
tmtlr1=readRDS("savedresults/ngse1_mtlrt.rds")*60
tdnnsurv1=readRDS("savedresults/ngse1_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse1_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse1_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse1_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse1_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse1_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse1_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse1_survivalsvmt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g23=plot_fun1(m)
g24=plot_fun2(t)
ngene1all=cbind.data.frame(m,t,m1,t1)
ngene1all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#ngene2
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse2_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse2_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse2_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse2_rsf1m.rds")
rsf2=readRDS("savedresults/ngse2_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse2_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse2_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse2_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse2_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse2_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse2_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse2_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse2_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse2_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse2_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/ngse2_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/ngse2_p_cox3t.rds")*60
trsf1=readRDS("savedresults/ngse2_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse2_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse2_mtlrt.rds")*3600
tdnnsurv1=readRDS("savedresults/ngse2_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse2_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse2_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse2_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse2_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse2_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse2_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse2_survivalsvmt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g25=plot_fun1(m)
g26=plot_fun2(t)
ngene2all=cbind.data.frame(m,t,m1,t1)
ngene2all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#ngene3
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse3_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse3_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse3_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse3_rsf1m.rds")
rsf2=readRDS("savedresults/ngse3_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse3_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse3_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse3_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse3_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse3_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse3_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse3_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse3_limma_coxboostm.rds")
survivalsvm=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse3_p_cox1t.rds")
tpcox2=readRDS("savedresults/ngse3_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/ngse3_p_cox3t.rds")
trsf1=readRDS("savedresults/ngse3_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse3_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse3_mtlrt.rds")*3600
tdnnsurv1=readRDS("savedresults/ngse3_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse3_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse3_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse3_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse3_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse3_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse3_limma_coxboostt.rds")
tsurvivalsvm=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g27=plot_fun1(m)
g28=plot_fun2(t)
ngene3all=cbind.data.frame(m,t,m1,t1)
ngene3all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#ngene4
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse4_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse4_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse4_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse4_rsf1m.rds")
rsf2=readRDS("savedresults/ngse4_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse4_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse4_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse4_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse4_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse4_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse4_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse4_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse4_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse4_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse4_p_cox1t.rds")
tpcox2=readRDS("savedresults/ngse4_p_cox2t.rds")
tpcox3=readRDS("savedresults/ngse4_p_cox3t.rds")
trsf1=readRDS("savedresults/ngse4_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse4_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse4_mtlrt.rds")*3600
tdnnsurv1=readRDS("savedresults/ngse4_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse4_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse4_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse4_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse4_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse4_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse4_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse4_survivalsvmt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g29=plot_fun1(m)
g30=plot_fun2(t)
ngene4all=cbind.data.frame(m,t,m1,t1)
ngene4all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#ngene5
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse5_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse5_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse5_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse5_rsf1m.rds")
rsf2=readRDS("savedresults/ngse5_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse5_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse5_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse5_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse5_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse5_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse5_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse5_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse5_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse5_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse5_p_cox1t.rds")
tpcox2=readRDS("savedresults/ngse5_p_cox2t.rds")
tpcox3=readRDS("savedresults/ngse5_p_cox3t.rds")
trsf1=readRDS("savedresults/ngse5_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse5_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse5_mtlrt.rds")*60
tdnnsurv1=readRDS("savedresults/ngse5_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse5_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse5_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse5_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse5_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse5_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse5_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse5_survivalsvmt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g31=plot_fun1(m)
g32=plot_fun2(t)
ngene5all=cbind.data.frame(m,t,m1,t1)
ngene5all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")
#ngene6
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse6_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse6_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse6_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse6_rsf1m.rds")
rsf2=readRDS("savedresults/ngse6_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse6_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse6_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse6_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse6_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse6_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse6_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse6_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse6_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse6_survivalsvmm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse6_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/ngse6_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/ngse6_p_cox3t.rds")*60
trsf1=readRDS("savedresults/ngse6_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse6_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse6_mtlrt.rds")*3600
tdnnsurv1=readRDS("savedresults/ngse6_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse6_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse6_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse6_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse6_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/ngse6_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse6_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse6_survivalsvmt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm)

m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g33=plot_fun1(m)
g34=plot_fun2(t)
ngene6all=cbind.data.frame(m,t,m1,t1)
ngene6all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm")

labelss<-c("pbc","veteran","lung","anz","us","melanoma_clinical","melanoma_itraq","melanoma_swath","melanoma_nano","gse1","gse2","ngene1","ngene2","ngene3","ngene4","ngene5","ngene6")
ggarrange(g1,g3,g5,g7,g9,g11,g13,g15,g17,g19,g21,g23,g25,g27,g29,g31,g33,labels = labelss,ncol = 3, nrow = 6)

ggarrange(g2,g4,g6,g8,g10,g12,g14,g16,g18,g20,g22,g24,g24,g26,g28,g30,g32,g34,labels = labelss,ncol = 3, nrow = 6)

5 all summary

data_list2=list(pbcall,veteranall,lungall,anzall,usall,melanomaclinicalall,melanomaitraqall,melanomaswathall,melanomananoall,gse1all,gse2all,ngene1all,ngene2all,ngene3all,ngene4all,ngene5all,ngene6all)
datafull2=bind_rows(data_list2, .id = "datasets")
datafull2$datasets=mapvalues(datafull2$datasets, from = seq(1,17,1), to = c("pbc","veteran","lung","anz","us","melanomaclinical","melanomaitraq","melanomaswath","melanomanano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
timememorydt=datafull2%>% group_by(methods) %>%dplyr::summarize(Mean_time = mean(t, na.rm=TRUE),Mean_memory=mean(m,na.rm = TRUE))

datafull3=datafull
datafull3$datasets=mapvalues(datafull3$datasets, from = seq(1,17,1), to = c("anz","us","veteran","lung","pbc","melanoma_clinical","melanomaitraq","melanomaswath","melanomanano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
predictiondt=datafull3%>% group_by(model,variable) %>%dplyr::summarize(Mean_value = mean(value, na.rm=TRUE), SD_value=sd(value,na.rm = TRUE))
predictiondt2=predictiondt[predictiondt$variable %in% c("hc", "unoc", "bs1","auc1", "auc5", "auc10", "auc15", "auc"),]
predictiondt3 <- tidyr::spread(predictiondt2[,1:3], variable, Mean_value)
predictiondt4=tidyr::spread(predictiondt2[,c(1,2,4)], variable, SD_value)


allsummarydt=cbind.data.frame(timememorydt,predictiondt3[,2:9],predictiondt4[,2:9])

colnames(allsummarydt)=c("methods","Mean_time","Mean_memory","Mean_hc","Mean_unoc","Mean_bs","Mean_auc1","Mean_auc5","Mean_auc10","Mean_auc15","Mean_iauc","SD_hc","SD_unoc","SD_bs","SD_auc1","SD_auc5","SD_auc10","SD_auc15","SD_iauc")

allsummarydt2=select(allsummarydt, -methods) %>% mutate_all(funs(dense_rank(desc(.))))
allsummarydt3=select(allsummarydt, -methods) %>% mutate_all(funs(rank(.)))
allsummarydt4=cbind.data.frame(allsummarydt2[,3:10],allsummarydt3[,c(1,2,11:18)])
rownames(allsummarydt4)=allsummarydt$methods


nb.cols <- 18
mycolors <- colorRampPalette(brewer.pal(8, "PuOr"))(nb.cols)

mat=as.matrix(allsummarydt4)
Heatmap(t(mat), name = "rank", col = mycolors,cluster_rows = FALSE,cluster_columns=TRUE,rect_gp = gpar(col = "white", lwd = 2))

indicator=read_excel("result.xlsx",sheet = 11)
pheatmap(indicator[,-1],cluster_cols = FALSE, cluster_rows = FALSE)